1. Field of Invention
The present invention relates to a method of adjusting a blind equalizer. More particularly, the present invention relates to a method of adjusting weights of a blind equalizer.
2. Description of Related Art
Many modern digital data communications systems employ adaptive equalization to compensate for the effects of changing conditions and disturbances on the signal transmission channel. Equalization removes intersymbol interference (ISI) caused by transmission limited channel bandwidth. ISI causes the value of a given symbol to be distorted by the values of proceeding and following symbols. The equalization function is typically performed by digital receiver circuits.
An adaptive equalizer is essentially an adaptive digital filter. In systems using an adaptive equalizer, it is necessary to provide a method of adapting the filter response so as to adequately compensate for channel distortions. Several algorithms are available for adapting the filter coefficients and thereby the filter response. For example, one widely used method employs the Least Mean Squares (LMS) algorithm. In this algorithm, by varying the coefficient values as a function of an error signal (E), the equalizer output signal is forced to approximately equal a reference data sequence. This error signal is formed by subtracting the equalizer output signal from the reference data sequence. As the error signal approaches zero, the equalizer approaches convergence to make the equalizer output signal and the reference data sequence being almost equal.
When the equalizer operation is initiated, the coefficient values (filter weights) are usually not set at values to achieve adequate compensation of channel distortions. In order to force initial convergence of the equalizer coefficients, a known “training” signal may be used as the reference signal. This signal is programmed at both the transmitter and receiver. The error signal is formed at the receiver by subtracting a locally generated copy of the training signal from the output of the adaptive equalizer. The training signal serves to open the initially “eye” of the received signal, as known in the art. After adaptation with the training signal, the eye has opened considerably and the equalizer is switched to a decision-directed operating mode. In this mode final convergence of the filter tap weights is achieved by using the actual values of symbols from the output of the equalizer instead of using the training signal. The decision-directed equalizing mode is capable of tracking and canceling time varying channel distortions more rapidly than methods using periodically transmitted training signals.
A problem arises when a training signal is not available. In such case “blind” equalization is often used to provide initial convergence of the equalizer coefficient values and to force the eye of the communication system to open. Blind equalization has been extensively studied for a long time.
The adaptation of the weights in adaptive equalizers is based on an assumed correct decision about which symbol was received. This assumption is valid for equalizers with a training sequence for which the received symbol is in fact known in advance. Equalizers, however, are also used without the benefit of a training sequence, in which case the decision is not necessarily correct. These equalizers are referred to as blind equalizers. The term blind refers to trying to find the correct equalizer coefficients without a reference training sequence, therefore during convergence the decisions may be incorrect and the coefficients (weights) erroneously updated. Although the possibility of a mistake exists, if the blind equalizer makes correct decisions for a sufficiently large set of received symbols, the equalizer will converge correctly.
Unfortunately, if the Least Mean Squares (LMS) algorithm is introduced to adjust the weights of a conventional equalizer. As the level of the error signal (E) increases, the degree of adjustment for W(n+1) increases correspondingly. If an original level of an input signal is very close to a slicer level of a slicer of the equalizer, it is easy to occur that the level of the input signal is incorrectly introduced to change the level of the error signal (E). In such case, the final level of the error signal (E) is inaccurate and the adjusted weights of the equalizer are also incorrect.